This study focused on the optimization of micro-milling parameters for two extensively used aerospace materials (titanium and nickel-based superalloy). The experiments were planned using Taguchi experimental design method, and the influences of spindle speed, feed rate and depth of cut on machining outputs, namely, tool wear, surface roughness and cutting forces, were determined. Tool wear, surface roughness and cutting forces measured in micro-milling of Ti6Al4V titanium alloy and Inconel 718 workpiece materials were optimized by employing Taguchi’s signal-to-noise ratio. The percentage contribution of micro-milling parameters, namely, spindle speed, feed rate and depth of cut, on tool wear, surface roughness and cutting forces was indicated by analysis of variance. The regression models identifying the relationship between the input variables and the output responses were also fitted using experimental data to predict output responses without conducting the experiments. Efficiency of regression models was determined using correlation coefficients, and the predicted values were compared with experimental results. From results, it was concluded that the established regression models could be employed for predicting tool wear, surface roughness and cutting forces in micro-milling of Ti6Al4V titanium alloy and Inconel 718 workpiece materials.